Speaker
Description
Artificial Intelligence methods show fantastic results in specific applications. However, Deep Learning approaches still suffer from the back-box phenomena such that it remains impossible to understand the way a model for a given taskworks. This is unproblematic in a large range of applications, but in safety critical environments, this changes drastically: if human lives are involved, some properties like predictiveness, provability and robustness can become of high importance and thenrepresent a competing objective during the engineering phase.
In this talk I will present some examples from our work at Fraunhofer FKIE, department of sensor data and information fusion, where we have been working on perception, data analytics, and sensor based situational awareness in both,civil and military applications. I will discuss examples from the fields of autonomous driving and surveillance and reconnaissance. Moreover, approaches from the AI certification community are presented to check for robustness and to make the engineering processtestable.
